Bridge Network Research Articles

Evolution of anisotropic capillarity in unsaturated granular media within the pendular regime

While the shear behavior of granular soils is directly related to the microstructure of contacts which often leads to the coaxiality between Cauchy stress and Satake fabric tensors, it is generally accepted by the geomechanics and geotechnical engineering community that the capillary effects are isotropic. At low saturation levels, however, the pore fluid tends to form interparticle menisci that can also manifest an anisotropic structure, which may result in the development of anisotropic capillarity in wetted granular media. To study the interplay between the solid grain contacts and the liquid bridges at the micro-scales, this study adopts a coupled discrete element method that utilizes a linear contact model combined with a capillary model, and explores their effects by conducting a series of numerical experiments. The distributions of contact and capillary force orientations during the experiment are further investigated to better understand how their alignments affect the global response of the granular assembly subjected to a deviatoric loading. The results indicate that the global shear stress response is not only affected by the contact fabric but also by the network of liquid bridges, and we also observe that the particles may lose contact while the pendular menisci may not be destroyed during the elastic unloading.

Key lifestyles and health outcomes across 16 prevalent chronic diseases: A network analysis of an international observational study.

Central and bridge nodes can drive significant overall improvements within their respective networks. We aimed to identify them in 16 prevalent chronic diseases during the coronavirus disease 2019 (COVID-19) pandemic to guide effective intervention strategies and appropriate resource allocation for most significant holistic lifestyle and health improvements. We surveyed 16 512 adults from July 2020 to August 2021 in 30 territories. Participants self-reported their medical histories and the perceived impact of COVID-19 on 18 lifestyle factors and 13 health outcomes. For each disease subgroup, we generated lifestyle, health outcome, and bridge networks. Variables with the highest centrality indices in each were identified central or bridge. We validated these networks using nonparametric and case-dropping subset bootstrapping and confirmed central and bridge variables' significantly higher indices through a centrality difference test. Among the 48 networks, 44 were validated (all correlation-stability coefficients >0.25). Six central lifestyle factors were identified: less consumption of snacks (for the chronic disease: anxiety), less sugary drinks (cancer, gastric ulcer, hypertension, insomnia, and pre-diabetes), less smoking tobacco (chronic obstructive pulmonary disease), frequency of exercise (depression and fatty liver disease), duration of exercise (irritable bowel syndrome), and overall amount of exercise (autoimmune disease, diabetes, eczema, heart attack, and high cholesterol). Two central health outcomes emerged: less emotional distress (chronic obstructive pulmonary disease, eczema, fatty liver disease, gastric ulcer, heart attack, high cholesterol, hypertension, insomnia, and pre-diabetes) and quality of life (anxiety, autoimmune disease, cancer, depression, diabetes, and irritable bowel syndrome). Four bridge lifestyles were identified: consumption of fruits and vegetables (diabetes, high cholesterol, hypertension, and insomnia), less duration of sitting (eczema, fatty liver disease, and heart attack), frequency of exercise (autoimmune disease, depression, and heart attack), and overall amount of exercise (anxiety, gastric ulcer, and insomnia). The centrality difference test showed the central and bridge variables had significantly higher centrality indices than others in their networks (P < 0.05). To effectively manage chronic diseases during the COVID-19 pandemic, enhanced interventions and optimised resource allocation toward central lifestyle factors, health outcomes, and bridge lifestyles are paramount. The key variables shared across chronic diseases emphasise the importance of coordinated intervention strategies.

An unsupervised machine learning approach for real-time damage detection in bridges

The bridge network is progressively aging, with an alarming proportion of bridges over 100 years. This situation engenders substantial risks to the overall reliability of transportation networks, requiring innovative methods for efficient management. Monitoring can provide a direct source of information about structural behavior generating alerts when changes occur. Real-time alerts enable effective infrastructure management and decision-making during damage or anomalous situations. However, monitoring can result in a large amount of data that is often difficult to convert into valuable information in real time. This paper presents an approach for real-time detection of abrupt damage occurrence in bridges using unsupervised anomaly detection algorithms and strain/acceleration measurements. The approach incorporates the separation of measurements into events having the same loading nature and the construction of three feature matrices based on statistical features, time-frequency features, and wavelet spectrum features. It includes the evaluation of five anomaly detection algorithms including Isolation Forest, One-Class Support Vector Machine, Robust Random Cut Forest, Local Outlier Factor, and Mahalanobis Distance. The approach is illustrated with a case study of a steel-bascule-railway bridge, that has experienced a brittle cracking event during monitoring. Results highlight the robustness of One-Class Support Vector Machine, Isolation Forest, and Local Outlier Factor algorithms in promptly detecting abrupt changes across different features. The separation of strain and acceleration data into loading-based events, coupled with the comparison of previous and new event features, provides robust feature matrices for effective damage detection. Enhanced detection and higher scores are particularly attributed to time-frequency domain features during damage occurrence. The presented approach can be used as a base on how to perform real-time anomaly detection within the context of bridge monitoring.

A risk-based quantitative approach for priority assessment of ageing bridges accounting for deterioration

The significant role of bridges in transportation networks requires continuous improvement of bridge management strategies, which has been even more exacerbated by the recent bridge collapses worldwide. This study proposes a framework for the prioritization and preliminary risk assessment of existing bridges subjected to seismic and traffic hazards. The proposed method adopts quantitative expressions to define risk in a simplified manner and, consequently, the priority associated with a certain bridge within a bridge stock. The three primary components of risk are considered: vulnerability, exposure, and hazard, which are specified according to the characteristics of the bridge, site, and road network. The bridge state of conservation is accounted for in the vulnerability component through average and critical defect condition indicators. The proposed method is implemented in a case-study of thirty real bridges in northern Italy. The impact of defects on vulnerability and priority ranking of bridges, the assumptions for defects condition and each subcomponent’s contribution to risk classification are discussed. The prioritization results are presented in absolute and normalized formats, with the latter providing a clearer view of the bridges’ relative priority within the stock. Finally, the proposed method’s findings are compared with the current Italian qualitative guidelines for bridge management.

Optimization of retrofit strategies for urban bridge networks before earthquake via dynamic-static integration

Addressing earthquake resistance in urban areas, the intervention and retrofit of bridge networks are crucial considerations. While previous studies have predominantly focused on the importance and retrofit aspects of bridges, there has been limited exploration of optimization issues. This study introduces a method for assessing node importance and optimizing retrofit strategies for bridge networks before earthquake. Combining the Personal PageRank (PPR) approach with the technique of ideal solution similarity order preference (TOPSIS) yields static importance evaluation results for nodes, reducing the subjectivity of their weights in multi-metric evaluation works. Employing a sandpile model to simulate post-earthquake traffic congestion, the static results are then refined based on the model. The study establishes a dynamic-static integration approach to assess bridge importance, and genetic algorithms are applied to optimize the retrofit strategy. The practicality of the dynamic and static integration evaluation methods is verified through a numerical example, demonstrating that the optimized strategy effectively improves retrofit performance.

Enhanced contact flexibility from nanoparticles in capillary suspensions

Hypothesis: Sample-spanning particle networks are used to induce structure and a yield stress, necessary for 3D printing of porous ceramics and paints. In capillary suspensions, a small quantity of immiscible secondary fluid is incorporated into a suspension. By further adding nanoparticles with a range of hydrophobicities, the structure of the bridges and microparticle-microparticle contacts is expected to be modified, resulting in a tunable yield stress and shear moduli. Moreover, the compressibility of these samples, important in many processing and application steps, is expected to be sensitive to these changes.Experiment: The nanoparticle hydrophobicity was altered and their position relative to the microparticles and the bridges was examined using confocal microscopy where the correlation between bridge size and network structure was observed. A step-wise uniaxial compression test on the confocal was conducted to monitor the microparticle movement and structural changes between capillary suspension networks with and without nanoparticles.Findings: Our observation suggests that nanoparticles induce the formation of thin liquid films on the surface of the microparticles, mitigating contact line pinning and promoting internal liquid exchange. Additionally, nanoparticles at microparticle contact regions further diminish Hertzian contact, enhancing the capacity for rearrangement. These effects enhance microparticle movement, narrowing the bridge size distribution.

On compensating the source resistance of the familiar Wien bridge networks

On compensating the source resistance of the familiar Wien bridge networks

Fragility Analysis and Resilience Assessment of the Single-Column Pier Steel-Concrete Composite Bridge Subjected to Seismic Loads

Abstract With the advantages of a small footprint, wide under-bridge view, and beautiful appearance, single-column pier bridges are widely used in urban bridge networks. However, single-column pier bridges are prone to damage during earthquakes or heavy vehicle use, which can seriously affect normal operations and postdisaster recoveries. Therefore, there is an urgent need to carry out seismic resilience assessments of single-column pier bridges and formulate disaster prevention and mitigation measures from the aspects of design, maintenance, and postearthquake recovery. This paper first establishes a resilience assessment framework for the single-column pier bridge and optimizes a functionality recovery model after an earthquake. Then, a numerical model of a sample bridge is built for resilience fragility analysis. Nonlinear dynamic time history analysis is performed to build a probabilistic seismic demand model, and moment–curvature analysis is performed to build a probabilistic seismic capacity model. Finally, a seismic resilience assessment of the single-column pier bridge is obtained based on the seismic fragility, and a sensitivity analysis is carried out for the pier height, pier section dimension, span and vehicle load level to improve the resilience of the single-column pier bridge.

Formation of small-granule starch oleogels based on capillary force: Impact of starch surface lipids on lubrication performance

Starch granule oleogels were prepared and their rheological properties were precisely tuned using the capillary bridging phenomenon. The addition of a small amount of water to an oily suspension of starch granules can lead to starch granule bridging and network formation, transitioning it from a fluid-like to a gel-like state. Small-granule starches with high specific surface area and interfacial area exhibited a greater number of liquid bridges and stronger starch granules interactions, making them more prone to forming structurally stable oleogel systems. By increasing the content of water and starch granule, the starch oleogels exhibited three distinct structural states: pendular state (water ≤ 3.28 %, starch ≤ 17.85 %), pendular bridging network (water: 4.92 %, starch: 24.59 %), and capillary aggregates (water ≥ 6.56 %, starch > 24.59 %). Furthermore, the influence of starch granule surface lipids on the lubrication performance of the oleogel system was investigated. Surface roughness increased after extraction of surface lipids, and the friction coefficient also showed a significant increase. Overall, capillary suspension system can potentially be used to design novel fat food products, and our findings have established the correlation between starch granule surface properties and sensory perception in food, providing valuable insights for adjusting the oral processing characteristics of food.

Impact of Climate Change on Risk Assessment and Effective Maintenance Strategies for Bridge Networks Subjected to Corrosion

Impact of Climate Change on Risk Assessment and Effective Maintenance Strategies for Bridge Networks Subjected to Corrosion

Analysis of End-to-End Delay in the Transport Segment of Fronthaul 4G/5G Networks Based on TSN Technology

One of the characteristic features of 4G/5G mobile networks is the spatial separation of functional blocks. The corresponding segments of the xHaul transport network are used to connect these blocks. One of them is the Fronthaul front end segment, which connects remote radio equipment with their control equipment. The data streams of standard CPRI/eCPRI radio interfaces in this segment impose strict requirements on the quality of service and, above all, on delays. To meet these requirements, it was proposed to use in the Fronthaul segment Ethernet bridge network based on the technology of time-sensitive networks TSN (Time Sensitive Networking), which provides determinated delays, reliable packet delivery and high accuracy of synchronization of nodes in the network. The IEEE 802.1CM standard describes profiles of TSN networks that defines the functions, options, configurations, default values, protocols and procedures of bridges, stations and local networks required to build the Fronthaul transport segment. The article presents a methodology for determining the maximum end-to-end traffic delays of standard CPRI/eCPRI radio interfaces in the Fronthaul segment of 4G/5G networks, built on the basis of TSN technology, in accordance with the requirements of IEEE 802.1CM standard. Two main components of end-to-end delay are identified ‒ delays in TSN bridges and delays in xEthernet channels. For high-priority traffic flows of CPRI/eCPRI radio interfaces in bridges, characteristic cases of mutual influence of flows arriving simultaneously at different input ports are given. An example of numerical calculation is given, which allowed to determine the permissible physical length of the Fronthaul segment at a given boundary end-to-end delay of transmission of high-priority traffic.

Enhancing fluorescence properties in transparent glass ceramics via optimized Gd2O3/Ga2O3 ratios and crystallization conditions

In this study, Gd2O3–Ga2O3-GeO2-Nd2O3 glasses and glass ceramics were synthesized, and their structural, thermal, and crystallization characteristics were analyzed. Our findings indicate that substituting Gd2O3 for Ga2O3 positively influences the enhancement of bridge oxygen content and network structure stability. Heat treatments at different temperatures showed no significant impact on the crystal phase type but did alter the crystallization rate. Optimal conditions were identified: a Gd2O3/Ga2O3 ratio of 0.6 facilitated the production of transparent glass ceramics containing a single-phase GdGaGe2O7 nano-crystal. Notably, the glass ceramic nucleated at 780 °C for 24 h and crystallized at 840 °C for 5 min exhibited superior performance. Despite a 6% reduction in transmission rate, the emission intensity at 1060 nm increased by 1.5 times. This material holds promise for various applications in solid-state lasers operating within the 1.06 μm region.

The unique salt bridge network in GlacPETase: a key to its stability.

The extensive accumulation of polyethylene terephthalate (PET) has become a critical environmental issue. PET hydrolases can break down PET into its building blocks. Recently, we identified a glacial PET hydrolase GlacPETase sharing less than 31% amino acid identity with any known PET hydrolases. In this study, the crystal structure of GlacPETase was determined at 1.8 Å resolution, revealing unique structural features including a distinctive N-terminal disulfide bond and a specific salt bridge network. Site-directed mutagenesis demonstrated that the disruption of the N-terminal disulfide bond did not reduce GlacPETase's thermostability or its catalytic activity on PET. However, mutations in the salt bridges resulted in changes in melting temperature ranging from -8°C to +2°C and the activity on PET ranging from 17.5% to 145.5% compared to the wild type. Molecular dynamics simulations revealed that these salt bridges stabilized the GlacPETase's structure by maintaining their surrounding structure. Phylogenetic analysis indicated that GlacPETase represented a distinct branch within PET hydrolases-like proteins, with the salt bridges and disulfide bonds in this branch being relatively conserved. This research contributed to the improvement of our comprehension of the structural mechanisms that dictate the thermostability of PET hydrolases, highlighting the diverse characteristics and adaptability observed within PET hydrolases.IMPORTANCEThe pervasive problem of polyethylene terephthalate (PET) pollution in various terrestrial and marine environments is widely acknowledged and continues to escalate. PET hydrolases, such as GlacPETase in this study, offered a solution for breaking down PET. Its unique origin and less than 31% identity with any known PET hydrolases have driven us to resolve its structure. Here, we report the correlation between its unique structure and biochemical properties, focusing on an N-terminal disulfide bond and specific salt bridges. Through site-directed mutagenesis experiments and molecular dynamics simulations, the roles of the N-terminal disulfide bond and salt bridges were elucidated in GlacPETase. This research enhanced our understanding of the role of salt bridges in the thermostability of PET hydrolases, providing a valuable reference for the future engineering of PET hydrolases.

Relation of connectome topology to brain volume across 103 mammalian species.

The brain connectome is an embedded network of anatomically interconnected brain regions, and the study of its topological organization in mammals has become of paramount importance due to its role in scaffolding brain function and behavior. Unlike many other observable networks, brain connections incur material and energetic cost, and their length and density are volumetrically constrained by the skull. Thus, an open question is how differences in brain volume impact connectome topology. We address this issue using the MaMI database, a diverse set of mammalian connectomes reconstructed from 201 animals, covering 103 species and 12 taxonomy orders, whose brain size varies over more than 4 orders of magnitude. Our analyses focus on relationships between volume and modular organization. After having identified modules through a multiresolution approach, we observed how connectivity features relate to the modular structure and how these relations vary across brain volume. We found that as the brain volume increases, modules become more spatially compact and dense, comprising more costly connections. Furthermore, we investigated how spatial embedding shapes network communication, finding that as brain volume increases, nodes' distance progressively impacts communication efficiency. We identified modes of variation in network communication policies, as smaller and bigger brains show higher efficiency in routing- and diffusion-based signaling, respectively. Finally, bridging network modularity and communication, we found that in larger brains, modular structure imposes stronger constraints on network signaling. Altogether, our results show that brain volume is systematically related to mammalian connectome topology and that spatial embedding imposes tighter restrictions on larger brains.

Environmental life cycle assessment (LCA) for design of climate-resilient bridges – a comprehensive review and a case study

The life cycle assessment (LCA) study of a bridge and bridge network would provide the environmental profile and hotspot information including the GHG emission of different life stages, among the components. Compared with a rapid adoption of Road LCA into the procurement process in the developed countries, Bridge LCA however remains a nascent area where a few studies conducted in North America. The critical issues of environmental profile, hotspots and benchmarks of bridges remain a challenge due to the complexity of bridge structures, data collection and unfamiliarity of LCA in the bridge community. To address the challenge, this study presents a comprehensive bibliometric analysis and review regarding life cycle environmental impacts assessment of bridge projects around the world to identify the research pattern in order to capture the areas of research needed inside this theme. As a proof of concept, this study continues with conducting an LCA case study of a Bridge Replacement Project on a Canadian signature highway, demonstrating the adoption of the LCA methodology and a framework to streamline the collection of data, to develop, present, and interpret the environmental impacts, in terms of the durability and service life of the bridge asset. The study found that stainless steel rebar decks outperformed black steel decks in terms of CO2 reduction by over 10%, with transport fleet impacts being a significant part of the bridge’s overall environmental impact, highlighting the need for diverse functional units in bridge life cycle assessment studies.

Management of road bridge networks in Italy by means of integrated SHM systems

Management of stocks with large number of bridges is a relevant issue for the Italian roadway system. In the last years the use of integrated SHM (Structural Health Monitoring) systems is being introduced within the framework of the modern BMS (Bridge Management Systems) with the aim of implementing a continuous damage identification strategy. In this framework, ambient vibration-based SHM has received most attention owing to its minimal intrusiveness, non-destructive character and global damage identification capabilities. A lot of research effort has been devoted in the last years to SHM techniques but ease of use, interpretability and minimal intervention of expert judgement are essential issues for the fruitful application of SHM in real and wide bridge networks. ANAS S.p.A., the main road managing Authority in Italy, is implementing a huge monitoring program on its bridge network with the main purpose of addressing the recent indications of the Italian guidelines for assessing and monitoring of bridges, and the University of Padova, together with the University of Perugia, Politecnico of Milan, University of Pisa and the other Universities of FABRE Consortium are supporting ANAS for the real application of modern SHM techniques. According to these actual needs, a new software suite called P3P has been developed and is being applied to a huge stock of bridges distributed in all the Italian territory.

An historical research on the development and application of prestressed concrete in Italy.

Given the diverse and rich morphology of the Italian Peninsula, bridges have played a major role in the development of this country starting from the Roman times with road systems like the Aurelia and the Cassia, still standing today.Since ancient times, the development of a road and a bridge infrastructure network has always been the expression of a State’s organizational structure and power, being tied to a military or trading nature.Without doubt, the advent of concrete has allowed to quickly build and connect the country from north to south by developing an intense network of roads and bridges. Prestressed concrete has played a crucial role in Italy’s infrastructure projects starting from the 1950’s with the construction of the Autostrada del Sole. Prestressed concrete represents a significant milestone in the evolution of bridge engineering, enabling the construction of structures with longer spans, increased durability, and enhanced structural performance. Through historical research, this study highlights the evolution of prestressed concrete technology in Italy, tracing its inception to early experiments and its subsequent refinement over the decades. The study explores the pioneering efforts of the Centro di Studio sugli Stati di Coazione Elastica at the Polytechnic of Turin for the development and application of prestressed concrete in Italy from 1945 to 1962. This study highlights key engineers, patents and case studies that have contributed to the technological advancement of prestressed concrete in Italy.

Fragility curves derivation under fire actions for different types of bridges

The Italian infrastructure network of bridges is complex, due to the orography of the territory and the presence of different hazard sources. Furthermore, during their service life these infrastructures can be subjected also to extreme actions such as the fire. This paper presents a procedure for the fragility curves derivation under fire actions for different types of bridges. The novelty the work is to apply the fragility assessment also to the fire resistance aspects, especially related to the infrastructures. Two case study pilot R.C. and composite steel-R.C. bridges are modelled by assigning combined loads according to the fire load combination and deriving structural response under fire scenarios. By using the linear regression, the structural response parameters of the bridges are correlated with fire intensity, considering different performance levels. Finally, lognormal fragility curves are obtained, measuring the capacity of the case study bridges. The study also shows which of the fire scenario parameters can be better correlated to the structural response of the bridges.

Simulation-based seismic risk and robustness assessment of aging bridge networks

Simulation-based seismic risk and robustness assessment of aging bridge networks

Dynamic characterization of a curved nine-spans pre-stressed concrete box girder bridge with half-joints

In recent years, the failure of many bridges has pressed governments all over the world to increase investments and to enact new monitoring regulations. In the Italian context, the Ministry of Infrastructure and Transport answered to that challenge by enacting the new Guidelines on Risk Classification and Management, Safety Assessment and Monitoring of Existing Bridges, in 2022. In this new regulation, Structural Health Monitoring (SHM) plays a fundamental role, complementing traditional periodic inspections by carrying out low intrusive non-destructive analyses. In this field, Operational Modal Analysis (OMA) is a vibration-based method consisting in the processing of the ambient accelerations recorded on the structure in order to extract and track its modal features. The final goal of continuous OMA is the timely identification of possible damage-induced persistent variations in the modal features of the monitored structure. This is why one of the most recent advances in OMA algorithms is related to the development of continuous Automated OMA embedded in high-potential software, to enable the infrastructure management authorities to realize large-scale monitoring of bridge networks. Many road bridges are however long multi-span bridges which require numerous sensors for their monitoring, thus making automated OMA especially challenging from the computational point of view. The aim of the work is to contribute to a more computationally affordable automated OMA for long bridges, by proposing to subdivide the OMA task considering suitable selections of subgroups of sensors. The proposed approach is illustrated in a case study consisting of a curved nine-spans pre-stressed concrete box girder bridge with 10 half-joints and equipped with more than 50 sensors.